Mars (Liyao) Gao![]() Ph.D. student About meI am a Ph.D. student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Professor J. Nathan Kutz. My research lies in AI for scientific discovery, with a focus on developing interpretable and generalizable learning frameworks for complex spatiotemporal and dynamical systems. I work at the intersection of symbolic regression, sparse modeling, and deep learning, aiming to uncover governing equations and enable reliable long-term prediction to accelerate scientific understanding. My long-term goal is to build robust machine learning methods that can bridge data and physical laws across domains like physics, climate science, fluid dynamics, neuroscience, and materials science. I am broadly interested in deep learning, statistical learning theory, Bayesian methods, time-series modeling, scientific computing, and recently agentic flow for science. News
[May. 2025]
Mesh-free SINDy paper collab and advised by amazing Bernat Font now out on arXiv!! [PDF]
New
[Apr. 2025]
Invited talk @ UCSB Applied Math seminar, UW CS4Env, and MIT in Marin Soljačić’s group.
[Mar. 2025]
Our newest work "Sparse identification of nonlinear dynamics and Koopman operators
with Shallow Recurrent Decoder Networks" with isotropic flow and convex loss landscape visualization is now available on arXiv! [Website] [Colab] [Github]
New
[Oct. 2024]
Invited talk @ Georgia Tech ACMS seminar.
[Mar. 2024]
Our paper "Bayesian autoencoders for data-driven discovery of coordinates, governing equations, and fundamental constants," is now published in PRSA!
Selected publications
ContactEmail: marsgao [at] uw [dot] edu |